Field
Embodiments described herein relate generally to a method of scatter correction of projection data, and more specifically to a method of scatter correction of X-ray projection data in a computed tomography scanner system.
Description of the Related Art
In general, an X-ray projection image contains many scattered radiation components. This scattered radiation greatly degrades the accuracy of a computed tomography (CT) value in three-dimensional imaging using a two-dimensional detector. A two-dimensional detector, like a flat-panel detector used in an X-ray diagnostic apparatus, uses a scattered-radiation-removing grid to suppress scattered radiation. The suppression of scattered radiation can be further improved by post processing the projection data using a scatter-correction algorithm. In an X-ray computed tomographic apparatus, a scatter-correction algorithm in conjunction with a scatter-suppressing grid yields superior images compared to scatter-suppressing grids alone because of residual scatter. Scattered radiation correction is indispensable for extracting low-contrast information, e.g., for imaging soft tissue, by using three-dimensional imaging using a two-dimensional detector.
In addition to the examples given above that discuss scatter scatter-suppressing grids and scattered radiation correction to improve the image quality of projection images and that also discuss improving the image quality of reconstructed images obtained from computed tomography on a series of projection images at different projection angles, scatter suppression can also be important for measurement geometries other than three-dimensional CT imaging using a two-dimensional detector. For example, the concepts and methods discussed herein also apply to a measurement geometry of two-dimensional CT imaging using a one-dimensional detector. The method of scatter correction can also apply when the projection data is not used for CT reconstruction.
An X-ray beam in the presence of a scattering object can be modeled as a primary X-ray beam P(x, y) and a scattered X-ray beam S(x, y), wherein the projection data T(x, y) is a composite of these two:T(x,y)=P(x,y)+S(x,y).Using a forward-scatter model, the scattered radiation S(x, y) is given byS(x,y)=SF(P(x,y))*G2(x,y),whereSF(X)=−X log(X), andG2(x,y)=A1exp[−α1(x2+y2)]+A2exp[−α2(x2+y2)]is a smoothing function that is a double Gaussian kernel with one term representing the coherent (Rayleigh) scattering and the other term representing the incoherent (Compton) scattering. The symbol “*” represents a convolution operator. The term with the coefficient A1 is obtained by modeling Rayleigh scattering, and the term with the coefficient A2 is obtained by modeling Compton scattering. In addition to expressing the physics of Rayleigh and Compton scattering, the double Gaussian kernel also expresses factors such as the geometry of the imaging device and the effectiveness of the scatter-suppressing grids. For example, the values of α1 and α2 depend on the aspect ratio of the scatter-suppressing grids. The “aspect ratio” is the height of the grid to its opening. In one implementation, in C-arm ASGs (anti-scatter grids) the aspect ratio can be approximately 10:1; while in diagnostic CT-scanners the aspect ratio can be approximately 30:1. These illustrative aspects ratios are non-limiting examples.
Given the above expressions, the total beam T(x, y) can be directly calculated from a known primary beam P(x, y), but it is impossible to analytically calculate the primary beam P(x, y) from a known total beam T(x, y). A conventional technique, therefore, calculates an estimate of the primary beam Pg (x, y) by minimizingE=|T(x,y)−Tg(X,y)|using a successive approximation method, where Tg(x, y) is a composite image calculated based on Pg(x, y), and can be represented byTg(x,y)=Pg(x,y)+Sg(x,y),where Sg(x, y)=−Pg(x, y) log Pg(x, y)*G2 (x, y), as discussed in U.S. Pat. No. 7,912,180, the contents of which are incorporated herein by reference.